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This study proposes a comprehensive data processing and modeling framework for building high-accuracy machine learning model to predict the steam consumption of a gas sweetening process. The data pipeline processes raw historical data of this application and identifies the minimum number of modeling variables required for this prediction in order to ease the applicability and practicality of such methods in the industrial units. On the modeling end, an empirical comparison of most of the state-of-the-arts regression algorithms was run in order to find the best fit to this specific case study. The ultimate goal is to leverage this model to identify the achievable energy conservation opportunity in such plants. The historical data for this modeling was collected from a gas treating plant at South Pars Gas Complex for 3 years from 2017 to 2019. This data gets passed through a multistage data processing scheme that conducts multicollinearity analysis and model-based feature selection. For model selection, a wide range of regression algorithms from different classes of regressor have been considered. Among all these methods, the Gradient Boosting Machines model outperformed the others and achieved the lowest crossvalidation error. The results show that this model can predict the steam consumption values with 98% R-squared accuracy on the holdout test set. Furthermore, the offline analysis demonstrates that there is a potential of 2% energy saving, equivalent to 24 000 metric tons of annual steam consumption reduction, which can be achieved by mapping the underperforming energy consumption states of the unit to the expected performances predicted by the model.

Cascaded humidification advanced turbine (CHAT) cycles are used for the enhancement of output power in conventional gas turbines. In this study, an IG–SOFC–CHAT combined cycle has been presented. The proposed cycle has been used as a novel approach for alleviating the restrictions on generated power in the SOFC–GT hybrid systems. Additionally, a significant amount of heat can be produced in this hybrid cycle, which justifies its application as a CHP system. Modeling and simulation of the cycle have been performed from the energy point of view. To generate syngas fuel from coal, an FICFB gasifier system, integrated with a low temperature gas cleaning system, has been used. Results show that compared to the conventional gas turbines, electrical power generation from the proposed IG–SOFC–CHAT cycle has been increased by about 45%, while the total energy efficiency of the comprehensive cycle is still within the acceptable range of CHP cycles. Moreover, results indicate that the output power from the proposed cycle is stable against ambient temperature variations. In addition, wastage of heat in the proposed cycle was minimized by transferring waste heat to heat sinks, which resulted in the balance of energy in the cycle. Overall results show the suitability of the proposed cycle to attain high power generation capacity in SOFC–GT hybrid systems. © 2016 American Institute of Chemical Engineers Environ Prog, 36: 610–618, 2017

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